Progress in Energy and Combustion Science 40 (2014) 59e73
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Progress in Energy and Combustion Science
journal homepage: www.elsevier.com/locate/pecs
Review
Agricultural residue production and potentials for energy and
materials services
Niclas Scott Bentsen a, *, Claus Felby a, Bo Jellesmark Thorsen b
a
University of Copenhagen, Faculty of Science, Department of Geosciences and Natural Resource Management, Rolighedsvej 23, DK-1958 Frederiksberg C,
Denmark
b
University of Copenhagen, Faculty of Science, Department of Food and Resource Economics, Rolighedsvej 25, DK-1958 Frederiksberg C, Denmark
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 28 August 2012
Accepted 20 September 2013
Available online 18 November 2013
Agricultural residues are potentially major contributors of resources for energy and material production.
We provide regional and global estimates of the amount of residues from major crops and address the
sources of uncertainty in the estimation of the amount of agricultural residues produced globally. Data and
methods available currently limit the use of resource estimates for energy or production planning. We
develop function based multipliers to estimate the global production of agricultural residues. The multipliers are applied to the production of the, on a global scale, six most important crops: barley, maize, rice,
soybean, sugar cane and wheat in 227 countries and territories of the world. We find a global production of
1
residues from these six crops of 3:7þ1:3
1:0 Pg dry matter yr . North and South America, Eastern, SouthEastern and Southern Asia and Eastern Europe each produce more than 200 Tg yr1. The theoretical energy potential from the selected crop residues is estimated to 65 EJ yr1 corresponding to 15% of the global
primary energy consumption or 66% of the world’s energy consumption for transport. Development towards high input agriculture can increase the global residue production by w1.3 Pg dry matter yr1.
Ó 2013 Elsevier Ltd. All rights reserved.
Keywords:
Agricultural residues
Residue production
Energy potential
Plant components
Contents
1.
2.
3.
4.
5.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.1.
Theory/calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.2.
Physiological constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.1.
Current residue production and energy potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2.
Current component production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.
Additional residue production potential through agricultural intensification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1.
Residue production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.
Component production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.
Energy potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4.
Additional residue production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.5.
Development and use of multipliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.6.
Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.7.
Current use of agricultural residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.8.
Resource availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.9.
Other biomass resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
* Corresponding author. Tel.: þ45 20 20 63 18.
E-mail address: [email protected] (N.S. Bentsen).
0360-1285/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.pecs.2013.09.003
60
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Nomenclature
Y
RP
RPR
HI
RY
IY
IRY
LHV
HHV
crop yield (kg ha1 yr1)
agricultural residue production (kg yr1)
residue to product ratio
harvest index
crop residue yield (kg ha1 yr1)
increased crop yield (kg ha1 yr1)
increased crop residue yield (kg ha1 yr1)
lower heating value (MJ kg1)
higher heating value (MJ kg1)
covers all 227 countries and territories in the FAO database. Results
are presented as aggregates into 22 geographical regions.
Six globally important crops in terms of production quantities are
considered [24]; barley, maize, rice, soybean, sugar cane and wheat.
Harvested area of these six crops covers 702 million ha corresponding to 50% of the world’s arable land (1411 million ha) [24]. In
FAO terms arable land include temporary crops (which covers the
crops selected here), temporary meadows and pastures and fallow
land. FAO does not provide a breakdown into these categories on a
global level. Crop residues comprise straw from barley, rice, soy bean
and wheat; stover from maize; and bagasse from sugar cane.
2.1. Theory/calculation
1. Introduction
The potential of biomass resources has been subject to
increasing research and debate over the recent years. International
and national agreements as the Kyoto Protocol [1] and EU Directives [2,3]; and policies as the European 20:20:20 Plan [4] and
the US Recovery and Reinvestment Act [5] have put substantial
pressure on politics promoting and sustaining the use of alternative
energy carriers. The steep increase in oil prices in 2008 [6] further
turned commercial attention towards alternative energy resources.
The United Nations estimates that the current population of 6.9
Billion will increase to 9.1 Billion by 2050 [7], with increased demand for food, materials and energy as a consequence. The International Energy Agency estimates that energy consumption will
increase with an expected 1.6% annual rate from 2005 to 2030 [8].
Biomass offers several options for displacing fossil resources [9e
11]. An important question for policy and planning purposes is
what amount of biomass resources is available. Several estimates of
agricultural crop residue production or potential have been published in peer reviewed journals over the last 20 years providing
different results. Estimates on global scale vary from 10 to 69 EJ yr1
in 2050 [12e15], differences owing to different methodology and
assumptions regarding residue production and ecological and
economic availability of the resource. A common trait of many
studies is the use of simple assumptions regarding residue production and ecological availability of agricultural residues. A
number of studies apply scalar multipliers to crop production to
estimate residue production [13,16e21], meaning that the amount
of residue from a crop is assumed proportional to the total production of the crop, and not on the yield per area unit of the crop. A
few apply function based multipliers [15,22,23], assuming that
residue production is proportional to crop yield. The assessment
regarding ecological and/or economic availability of crop residues
tends to build on crude assumptions and often fixed scalar recovery
rates used at national, regional or global level.
In this analysis we develop function based multipliers for estimating agricultural residue production based on crop yield. We
apply the indicators on the global production of barley, maize, rice,
soybean, sugar cane and wheat and estimate residue potentials for
22 geographical regions of the world. Also we estimate residue
yields potentially achievable through agricultural intensification,
and perform an analysis of the sensitivity of estimates.
Residue production (RP) (kg yr1) for crop j in country i is
calculated as:
RPij ¼ Aij $RYij ;
where A is harvested area and RY (kg yr1) residue yield by country
and crop.
Residue yield is calculated from the residue to product ratio (RPR)
for crop j. Empirical evidence suggests that RPR is not constant but
proportional to yield (Y) (kg ha1 yr1) [22,23,25]. Breeding in the
20th century has, specifically for cereals, increased the harvest index
without significant changes in total biomass production [26] indicating an asymptotic development towards a theoretical limit
determined by physiological constraints. This suggests an exponential relation between crop yield and residue yield, which is also
shown by Scarlat et al. [23]. We assume a relation of the general form:
RPRðYÞ ¼ aebY ;
(2)
with residue yield calculated as:
RYðYÞ ¼ Y$aebY :
(3)
For a > 0 and b < 0 function (3) will, with increasing Y, decrease
after a certain point and converge asymptotically toward zero. Such
a development is not consistent with empirical evidence [27].
Residue yields tend to increase to a certain level with increasing
crop yields and remain, in practice, constant hereafter. Building on
the above assumptions we apply a piecewise continuous model for
estimating residue yield as a function of crop yield for barley, maize,
rice, soybean and wheat (see also Fig. 2):
8
9
bj Yij for 0 Y 1 >
>
$a
e
Y
<
ij
j
ij
bj =
RYij Yij ¼
ca > 0; b < 0:
aj
>
>
:
;
for Yij > 1
be
b
j
(4)
j
Yield experiments, probably also including measurements of
residue production, are carried out in many countries by national
agricultural extension services. Such data are, however, not readily
accessible. Consequently we estimate parameters a and b in
equation (2) on the basis of tabular data published in peer reviewed
papers over the last 15e20 years (Table 1). The review has been
limited to literature in English and data are evaluated as representing a fraction of published data. When RPR is not provided
directly, RPR is calculated from harvest indices (HI) for each crop:
2. Materials and methods
RPR ¼
The assessment of agricultural production and residue potential
builds on statistics from the Food and Agriculture Organization of
the United Nations [24]. Harvested areas and crop yields are taken
as averages over the three year period 2006e2008. The assessment
(1)
1 HI
RY
¼
:
HI
Y
(5)
Harvest index is a measure of a plants partitioning of aboveground biomass into crop (grain) and other biomass. It expresses
the weight of the crop relative to the total aboveground biomass
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
61
Fig. 1. RPR functions and 95% prediction bounds for the fitting function for barley, maize, rice, soybean, sugar cane and wheat. Due to the methodology applied to estimate the RPR
function for sugar cane no confidence interval can be computed. Observations are based on references presented in Table 1.
[28]. Parameters and 95% prediction bounds of RPR functions are
estimated with a non-linear least squares fitting algorithm in
MatLab.
As a sterile plant, sugar cane differs from the other crops
included in this analysis as the total biomass production is not
partitioned between seeds and supportive tissues. Here we apply a
constant RPR ¼ 0.6 on dry weight basis in line with [29e31].
2.2. Physiological constraints
Plant physiology limits the range of residue to product ratios or
harvest indices as the residue part of a crop provides physical
support to the productive apparatus, mainly leaves, and to the crop
itself [28]. A theoretical limit for harvest index for wheat is
estimated to 0.60e0.65 (RPR ¼ 0.670.54) [52,53]. In maize the
harvest index has developed very moderately between the 1930s
and 1980 suggesting a limit of w0.55 (RPR ¼ 0.82) [28]. Data from
Khush [54] and Prasad et al. [55] suggest a harvest index limit for
rice of w0.60. The developed functions for estimating residue yield
(4) and their parameterization (Fig. 1) do not violate the above
described plant physiological constraints. A graphical representation of the model (4) developed here for estimating crop residue
production and the ranges of crop reported yields used in the
analysis are shown in Fig. 2.
Chrispeels and Sadava [56] report that average crop yield across
a range of crops including barley, maize, wheat and soybean are
21.5% of maximum attainable yield. This yield gap can be caused by
various factors as nutrient deficiency, water shortage, pests,
62
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Fig. 2. Modeled relation between crop yield and residue yield for the six crops included in the analysis: barley, maize, rice, soybean and wheat in panel a, and sugar cane in panel b.
The extension of individual curves represent the approximate range of crop yields reported by FAO (rounded to nearest 250 kg ha1 yr1) and included in the analyses. The graph is
based on the dry weight of crop and residue.
diseases or competition; either alone or in combinations. There is in
many cases significant potential for increasing crop yields and
consequently crop residue production. The potential for increasing
residue production through agricultural intensification is estimated
via a FAO/IIASA database on potential crop yields based on global
agro-ecological zoning (GAEZ). Fischer et al. [57] provided estimates of availability of suitable land and crop yields for different
levels of agronomic input. We apply a scenario for high input
agricultural practices under rain fed conditions, thus providing an
estimate of crop and residue production potentials, if every country
had access to improved seeds, adequate fertilization and technology. As crop yield potentials we use estimates of average yield on
the range of lands classified as very suitable to moderately suitable
(VS-MS) if the potential amount of suitable land is bigger than the
actual amount of land allocated to a specific crop. If not, we use
yield estimates for the land suitability range very suitable to
marginally suitable (VS-mS). For some country-crop combinations
FAO/IIASA estimate a zero yield potential in contradiction with the
FAOSTAT database on actual yields [24]. In that situation we assume
that improved yield potentials equal reported current yields. The
FAO/IIASA database includes 158 countries. For countries not
included in the database we also assume that yield potentials equal
current yields. The missing links between the FAOSTAT and FAO/
IIASA databases is evaluated to have only marginal impact on the
results. In mathematical terms, improved residue yield (IRY) (kg
yr1) is estimated as:
Table 1
Data references used for parameterization of residue to product ratio functions.
Location
Year(s)
No. of cultivars
Fertilizer application
(kg N ha1)
Crop type
Experimental design
No. of replicates
Ref.
Barley
Finland
USA
Denmark
1996e98
1984e99
1994e96
1
1
80,120
70
Spring
Spring
Winter/spring
3
4
[32]
[33]
[34]
Maize
Mexico
1975e93
64
0, 200
Landrace/improved
Malawi
USA, Spain,
Germany
USA
India
China
China
Thailand
Bangladesh
China
1997e99
1920e98
95
9e189
Landrace/hybrid
Landrace/hybrid
1994e96
1998e99
2000
2001
1995
1994e95
2005e07
1
1
118
191
6
2
2
USA
Greece
USA
Finland
Syria
China
Brazil
USA
Denmark
1992e93
1992e93
1986e87
1996e98
1991e96
2000e06
1995
1984e2004
2008e09
1
1
12
1
4
RCBD
RCBD
Review of 416
measurements
Review of 2
experiments
RCBD
Review of 9
experiments
SP
RCBD
RCBD
RCBD
SP
SP
Review of 3
experiments
RCBD
SP
RCBD
RCBD
SP
RCBD
RCBD
RCBD
RCBD
Rice
Soybean
Wheat
1
2
10
190e200
210
0, 30, 60
0, 90, 135
150, 300
Hybrid
RIL
RIL
Hybrid
0, 120, 240
80,120
0, 100
60
70
Spring
Winter
Winter
Spring
Winter
RIL ¼ recombinant inbred lines, RCDB ¼ randomized complete block design, SP ¼ split plot designs.
[35]
4
[35]
[36]
4
3
2
2
4
4
3e4
[37]
[38]
[39]
[39]
[40,41]
[42]
[43]
9
4
3
3
4
4
5
4
4
[44]
[45]
[46]
[32]
[47]
[48]
[49]
[33]
[50,51]
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
63
9
IYij;VSMS $RPRj for Aij;VSMS > Aij
=
IYij;VSmS $RPRj for Aij;VSMS Aij
;
IRYij ¼
;
:
Yij $RPRj for IYij 0 or country not included in FAO=IIASA database
8
<
Global production of plant components, cellulose, hemicellulose
and lignin is based on data on composition of individual biomass
fractions (Table 2). The composition of sugar cane bagasse and
maize stover is based on average values from the US DoE Biomass
Feedstock Composition and Property Database [58]. The composition of remaining residue species is based on average values from
the Dutch Phyllis database [59]. Energy potential from agricultural
residues is calculated as lower heating value (LHV) based on
[59,60].
(6)
mechanization, mineral fertilizers and plant protection but,
importantly, not through irrigation.
The total global potential of additional residue production is
1
estimated to 1:3þ1:0
(dry matter). Particularly in South Asia
0:6 Pg yr
there is a potential for increasing crop production and, consequently, residue production, but significant increases in crop and
residue production could also be achieved in South East Asia and
Eastern Europe (dominated by Russia).
4. Discussion
3. Results
4.1. Residue production
3.1. Current residue production and energy potential
The current (2006e08) production of residues from barley,
maize, rice, soybean, sugar cane and wheat is estimated to a total of
1
3:7þ1:3
1:0 Pg dry matter yr . Maize, rice and wheat account for more
than three quarters of the total production (Fig. 3).
Geographical regions standing out are North and South America,
Eastern and Southern Asia with a residue production of more than
500 Tg yr1 each. South-Eastern Asia and Eastern Europe have an
estimated residue production of more than 200 Tg yr1 each (Fig. 4,
Table 2). The theoretical energy potential from the selected crop
residues totals 65 EJ yr1. Geographically energy potentials range
from zero in Polynesia and Micronesia to more than 11 EJ yr1 in
North America and Eastern Asia (Table 3). Polynesia and Micronesia
fall out because their agricultural sector is focused on production of
banana, cassava and coconut as their main commodities [24] and
not cereals and pulses, which is covered in this analysis.
3.2. Current component production
The global production of cellulose, hemicellulose and lignin
from barley, maize, rice, soy bean, sugar cane and wheat residues
amounts to 1.376, 848 and 666 Tg yr1 respectively (Fig. 5). Regions
standing out are the same as above (North and South America,
Eastern, South-Eastern and Southern Asia and Eastern Europe).
3.3. Additional residue production potential through agricultural
intensification
Additional agricultural residue production is possible through
development towards high input agriculture (Fig. 6, Table 4). High
input meaning the use of improved seed sources, agricultural
Here we estimate the current (2006e08) total annual residue
production from barley, maize, rice, soy bean, sugar cane and wheat
1
to 3:7þ1:3
1:0 Pg yr . Smil [53] estimated the total global production in
the mid 1990’s to between 3.5 and 4.0 Pg yr1, with cereals, sugar
crops and oil crops accounting for 79% of the total production.
Accordingly, the estimate by Smil for the crops included in this
analysis would be in the approximate range 2.8e3.2 Pg yr1, which
is lower than our estimate. A later study by Lal [61] estimates the
global residue production to 3.4 Pg yr1 in 1991 and 3.8 Pg yr1 in
2001, with cereals accounting for 74e75% of the total and the six
crops analyzed here accounting for 78e82%. With reference to year
2000, Krausmann et al. [62] estimate the global residue production
to 4.4 Pg yr1. With the period 1997e2006 as a reference, Hakala
et al. [63] find the global production of residues to 5.4 Pg yr1, i.e.
considerably higher than other estimates and the one presented
here. Hakala et al. build their estimate on crop specific harvest
indices in contrast to residue to product ratios. Building on harvest
indices results in higher estimates than residue to product ratios
(see Section 4.4). Hakala et al. assume the harvestable part of the
residues to be 71% of the total aboveground production yielding a
production comparable to the above discussed literature of
3.8 Pg yr1. All figures discussed refer to the dry weight of biomass.
Wirsenius et al. [64] report the global residue production in
energy terms. For the reference period 1992e94 they estimate the
production to 61 EJ yr1, slightly lower than our estimate of
65 EJ yr1. However, their estimate is based on the higher heating
value (HHV) of biomass, where ours is based on the lower heating
value (LHV). HHV of biomass materials is typically 5e10% higher
than LHV depending on the hydrogen content of the material.
There is a tendency, although not equivocal, that estimates of
the global residue production depend on the applied reference year
Table 2
Moisture content, composition and heating value of crops and crop residues included in the analysis.
MC crop
Crop
Barley
Maize
Rice
Soy bean
Sugar cane (raw)
Wheat
0.15
0.15
0.15
0.15
0.75
0.15
MC residue
0.15
0.15
0.15
0.15
0.50
0.15
MC ¼ moisture content, LHV ¼ lower heating value.
Residue composition (dry weight basis)
Cellulose
Hemicellulose
Lignin
0.46
0.35
0.36
0.40
0.39
0.38
0.23
0.23
0.24
0.16
0.23
0.27
0.16
0.19
0.16
0.16
0.24
0.18
LHV (MJ kg1)
Ref.
18.2
17.9
17.5
17.7
18.0
18.2
[59]
[58,59]
[59]
[59,60]
[58,59]
[59]
64
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Fig. 3. Estimated annual global production of crop residue dry matter from barley, maize, soybean, sugar cane and wheat. Solid bars represent modeled values and error lines 95%
confidence intervals. No confidence interval is attributed to sugar cane residue production due to the methodology applied; see Section 2.1.
(Fig. 7). Across the estimates reported by Smil [53], Krausmann
et al. [62], Lal [61], Hakala et al. [63] there is an annual increase in
residue production of w1.5%. This corresponds well with the general increase in crop yields experienced, combined with a minor
increase in the agricultural area harvested since 1990 [65]. These
issues alone probably do not explain that our estimate is higher
than earlier published estimates. The estimates by Smil [53],
Krausmann et al. [62], Lal [61], Hakala et al. [63] and Wirsenius et al.
[64] are based on crop specific scalar or geographically stratified
crop specific scalar multipliers to crop yield in contrast to the
methodological approach in this study.
4.2. Component production
72e86% of the residue resource is made up of carbohydrate
(cellulose and hemicellulose) and phenolic polymers (lignin)
(Table 2) each of which may substitute fossil resources. One option
for carbohydrate processing is through biochemical conversion into
ethanol [66] or other alcohols. Thermochemical conversion of carbohydrates produces hydrogen and carbon monoxide, which can be
catalytically reformed to methanol. Fast pyrolysis and catalytic
conversion of lignin may be used to produce phenol, styrene,
benzene and toluene [9]. Task 42 of IEA Bioenergy estimate the
global production of bio-based chemicals and polymers to w50
million tons annually, and in recent years the market for a number
of bio-based bulk chemicals has experienced a significant growth in
the range of 10e30% annually [67].
4.3. Energy potential
The potential of bioenergy from agricultural residues has been
estimated in a number of studies. For 2010, agricultural residue
potentials are estimated to w17 EJ yr1 [68,69]. Smeets et al. [15]
estimate the 2050 potential to 49e69 EJ yr1, which is corroborated by Haberl et al. [13] (49 EJ yr1). The Global Energy Assessment (GEA) [70] estimate the year 2005 energy potential from crop
residues to 34 EJ yr1 (technical potential) or 78 EJ yr1 (theoretical
potential). Corresponding figures for year 2050 are 49 and
107 EJ yr1 respectively. The recent IPCC special report on renewable energy sources and climate change mitigation (SRREN) [71]
finds a technical energy potential from agricultural residues of
15e70 EJ yr1.
Fig. 4. Geographical distribution of estimated current (2006e08) production of residues from barley, maize, rice, soybean, sugar cane and wheat production.
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Comparisons between energy potentials from different studies
require caution as ‘potential’ can be defined in several ways [72,73].
The potential presented here is theoretical and thus comparable to
the potential presented by GEA [70]. Assuming that our central
estimate of 65 EJ yr1 covers 80% of the total residue production, it
is comparable to their estimate of 78 EJ yr1. Estimates of the
technical, economical or ecological/sustainable energy potential all
build on the theoretical potential, but are most often lower as
various constraints on the exploitation rate are included in the
models [73] (see Section 4.8).
The estimated theoretical energy potential from agricultural
residues of 65 EJ corresponds to 15% of the current global energy
consumption. A thorough review of the best use of crop residues is
beyond the scope of this analysis, but opinions diverge. On one
hand crop residues are considered a favorable feed-stock for
ethanol production due to the relatively low lignin content [74] and
low recalcitrance to bioconversion compared to more lignified
material [75,76]. Fermentation of crop residues offers a relatively
easy pathway to liquid fuels and to address GHG emissions and fuel
supply security in the transport sector. On the other hand a
pathway through combined heat and power production may be
preferable as it offers higher GHG emission reductions than straw
to ethanol or straw to Fischer-Tropsch liquids pathways [72]. The
current (2010) energy use for transport is 99 EJ yr1 [77] and is in
the International Energy Agency’s 2010 new policy outlook [78]
estimated to increase to app. 190 EJ by 2035. Not only the best
use of biomass for energy is debated, also bioenergy’s potential for
displacing fossil energy resources is questioned. A recent analysis
by York [79] finds that additional production of renewable energy
doesn’t displace fossil energy in a 1:1 ratio. The results suggest that
renewable energy resources tend to displace each other rather than
the fossil resources they were intended to. However, the analysis is
retrospective and doesn’t inform about future developments in the
energy supply.
4.4. Additional residue production
Agriculture offers two options for increasing the production of
residues: intensification (producing more per unit of land) and/or
expansion (producing on more units of land). Here we focus on
intensification. The global potential increase in residue production
1
is estimated to 1:3þ1:0
0:6 Pg yr . Southern Asia exhibits a huge potential in increasing its residue production with close to
300 Tg yr1. Also Eastern Africa, South America, Eastern Asia and
Eastern Europe exhibit potentials above 100 Tg yr1. In general it is
the lesser developed regions of the world that has the largest unutilized potential, whereas North, South and West Europe respectively have limited options for increasing residue production
through intensification, as also discussed by Haberl et al. [65].
The potential additional residue production estimated here does
not refer to any particularly year, but implementing it would
require significant efforts and developments in technology, agricultural practices and plant material, and hence time. Long term
projections regarding biological production are sensitive to potential impacts of e.g. changing climates and diets.
Haberl et al. [65] model the impact on crop yield of climate
change in 2050 and reach different conclusions depending on underlying assumptions. If the effect of elevated CO2 levels in the
atmosphere (CO2 fertilization) can be fully exploited they find a
yield increase in all regions with a global area weighted mean
of þ14.8% from 2000 to 2050. If CO2 fertilization cannot be
exploited, due to e.g. nutrient shortage, the global crop yield is
modeled to decrease by 7.1%, with only Central Asia, Russia and
Western Europe benefiting from climate change. For Europe, Olesen
and Bindi [80] report similar findings, particularly that Northern
65
European agriculture may benefit more from climate change than
Southern Europe.
Dietary changes may also influence the production of agricultural residues as well as the availability of residues. Krausmann
et al. [62] and Wirsenius [64] show that 65e70% of the biomass
harvested from agricultural lands is used to feed and bed livestock.
Also the Food and Agriculture Organization of the United Nations
(FAO) find that livestock accounts for 70% of all agricultural land
[81]. Changing diets may have counteracting effects on residue
production and availability. On one hand reduced meat consumption reduces the demand for feed crops and thus the amount of
crop residues. On the other hand it reduces the appropriation of
crop residues for feed and bedding and, thus, increases the fraction
of residues available for purposes other than for livestock. Haberl
et al. [65] find that a switch to a “fair and frugal” diet increases the
amount of crop residues available for energy purposes by more
than 20% as compared to their business as usual scenario.
Our estimates of residue production through agricultural
intensification build on modeling of attainable yields under rain fed
conditions. Higher yield could be achieved through widespread use
of irrigation. Much concern has, however, been raised over agricultural use of water resources [82]. The Aquastat database under
FAO estimate that 70% of the world’s fresh water use is accounted
for by agriculture [83]. Regional variation is apparent. In Near East
and North Africa agriculture use 51% of the renewable fresh water
resource and 40% of this is used for irrigation. South Asia exhibits a
significant draw on fresh water resources for agriculture and irrigation. In Latin America, irrigation accounts for 24% of water use by
agriculture. Agriculture, however, withdraw only 1% of the
renewable water resource. Applying a rain fed scenario to estimate
potential residue production under high input agricultural practices thus provides a conservative estimate and may, for specific
crops in specific regions, yield an estimate of attainable residue
production lower than current residue production.
Tilman et al. [84] report that agricultural expansion required to
feed the population in 2050 is additionally 890 million ha
compared to year 2000. Pasture is expected to make up the majority. Smeets et al. [15] estimate potential expansion of agricultural
land by 2050 in four scenarios to 729-3585 million ha; the latter
including landless animal production and extensive use of irrigation. Much concern has also been raised over land use changes
caused by agricultural expansion driven either by increased food
demand or biofuel demand. It has been shown that fuel demand
driven expansions may actually increase GHG emission [85] and
introduce a carbon debt, which may take centuries to pay back
depending on what kind of land is converted into agriculture
[86,87]. The area needed to replace global consumption of oil with
biofuels is estimated to be between 104 and 3142 million ha,
depending on crop yield and the proportion of biomass converted
to biofuel [88]. If 50% of a crop yield of 10 Mg ha1 is converted 786
million ha is needed.
To the extent agricultural residues can be harvested without
impairing soil quality and feed production, the land use impacts of
an increased use of the resource must be expected to be only
limited or absent (see also Section 4.8).
4.5. Development and use of multipliers
A variety of methodological approaches has been used to estimate crop residue production on national, regional or global scale. A
majority of studies apply species specific constant multipliers to
crop production statistics to calculate the residue production
[13,16e21,53,61,63,64]. This means that residue production is
assumed proportional to the total production of crops of different
species (barley, wheat, rice etc.) in a given country, region or
66
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Table 3
Estimated current (2006e08) annual production of crop residues in Tg (million tons) dry matter, and the theoretical energy potential from the residues. Missing numbers
indicate values below 0.05.
Africa
EasternBarley (Tg)
Upper confidence limit
Lower confidence limit
Maize (Tg)
Upper confidence limit
Lower confidence limit
Rice (Tg)
Upper confidence limit
Lower confidence limit
Soybean (Tg)
Upper confidence limit
Lower confidence limit
Sugar cane (Tg)
Wheat (Tg)
Upper confidence limit
Lower confidence limit
Total (Tg)
Upper confidence limit
Lower confidence limit
Theoretical energy
potential (EJ)
Upper confidence limit
Lower confidence limit
America
Middle -
Northern-
Southern0.2
0.3
0.2
15
20
10
0.8
1.0
0.6
3.8
2.9
3.4
2.4
22
28
17
0.4
25
32
19
17
13
12
1.8
2.2
1.4
0.8
0.1
0.1
0.1
45
48
34
0.8
0.5
0.3
1.0
0.6
1.9
2.4
1.5
37
47
28
9.0
7.2
6.4
1.0
1.3
0.8
4.8
4.9
5.6
4.1
58
68
45
1.0
8.0
9.3
6.3
0.1
4.6
5.7
3.6
7.7
12
4.7
7.3
9.4
3.9
0.1
0.1
0.0
3.7
20
24
17
44
55
33
0.8
1.3
0.8
0.2
0.1
1.0
0.6
6.2
7.7
4.7
1.1
0.8
0.8
0.1
0.1
0.1
0.7
globally. Krausmann et al. [62] add to the above assumption a
geographical stratification of the species specific constant multipliers assuming that crop residue production not only is proportional to total crop production, but also to the location of crop
production. A different approach is followed by e.g. Smeets et al.
[15], Fischer et al. [22], and Bentsen et al. [89]. They build their estimates of crop residue production on species specific linear multipliers assuming that crop residue production is proportional to
crop yield (production per area unit) rather than total crop production. A further development of this approach is demonstrated by
Scarlat et al. [23] in their estimate of the European Union crop residue potential. They apply a species specific logarithmic multiplier
to reported crop yields to calculate residue production. The methodological approach taken in this analysis is in line with that of
Scarlat et al. but builds on considerably more observations and on
physiological theory and on agricultural development showing that
increased yield is caused not only by an increased biomass production but also by a change in biomass partitioning between crop
and residue (increased harvest index) [28]. Scalar multipliers
applied to crop yield may be adequate when looking at well-defined
production systems as e.g. a specific crop in a specific climatic region. However, in a geographically and thus climatically and agronomically broader perspective scalar multipliers seem inadequate.
Based on the present work we suggest that further development in
crop residue assessments should focus on stratified species or
cultivar specific exponential (or logarithmic) multipliers, with
stratification based on ecological or political zones. Such an
approach would require significantly more data than is readily
available in the scientific literature but would be able to capture
more of the variability caused by differences in climate, soil types,
weather conditions or politically mandated restrictions e.g. on fertilizer use.
The model developed here predicts that crop residue yield increases logarithmically with increasing crop yield until a certain
level of crop yield and remains constant with further increases in
crop yield. The concave part of function (4) is parameterized via the
references presented in Table 1 and there is sound empirical
Western-
Caribbean
Asia
Central-
North-
South-
6.5
6.6
5.6
0.1
0.9
1.2
0.6
46
61
33
1.8
1.6
1.2
0.4
0.5
0.3
14
3.9
4.8
3.1
68
84
53
1.2
16
22
11
309
511
181
11
12
6.2
179
243
127
4.1
114
133
95
632
926
425
11.6
2.7
3.6
1.9
125
174
87
34
32
22
240
326
171
100
29
34
24
530
668
405
9.7
36
42
31
43
50
36
0.8
0.1
0.1
1.6
1.0
17.0
7.8
12.6
7.4
0.9
0.6
1.2
1.5
0.9
1.9
1.7
1.3
3.4
Central3.5
4.3
2.7
1.8
2.5
1.2
1.1
0.9
0.7
0.2
0.3
0.2
evidence for the relation between crop yield and residue yield in
within the range of crop yields between 0 and 1=bj (see Fig. 1 for
the crop specific parameter b). For crop yields exceeding 1=bj , i.e.
the horizontal part of the model, the empirical evidence is less
solid. The influence of this model limitation on the results presented here is limited. The estimates of the current residue potentials are for more than 99.9% of the total residue production built
on crop yield values between 0 and 1=bj . Correspondingly for the
estimates of potential additional residue production more than 99%
of the modeled residue production build on crop yield values between 0 and 1=bj . The results show that regions currently experiencing very high crop yields have limited potential of further
increasing crop yields, while it is in the regions experiencing low
and moderate yields that considerable increases could be expected.
Although insignificant for the results presented here future crop
residue assessments applying the methodology suggested here
would benefit from further exploring the relation between crop
yield and residue yield in very high yielding scenarios e.g. under
assumptions of irrigation.
Using harvest index (HI) as a basis for calculating the residue to
product ratio (RPR) may overestimate the ratio. Measured RPRs does
not account for a proportion of biomass stored in stubble. Measured
HI ideally includes this fraction as crops are harvested at soil surface
level. The proportion of biomass stored in stubble is influenced by
harvest technology. We have not applied corrections to data based
on measured HI as the literature base behind our RPR functions do
not provide information detailed enough to support specific assumptions. Hay [26] indicates that excluding stubble of 10 cm on tall
and semi-dwarf varieties may induce a bias on HI of up to 5%.
Data from Pedersen [50,51] and Smil [53] suggest that variations
in RPR cannot be attributed to crop yield alone. RPR varies not only
between species in the same tribe as shown here for barley and
wheat but also between cultivars of the same species [25].
Furthermore annual variations in RPR are found within cultivars on
the same location, and also location itself is shown to influence RPR
[25], indicating that residue production also depends on factors as
soil type, weather conditions, fertilizer regimes (not only
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Asia
Eastern-
Europe
South-Eastern-
Southern-
Western-
3.8
5.3
2.6
225
323
150
274
284
167
39
51
30
17
125
151
101
684
833
467
11.7
51
69
36
285
256
189
4.0
5.1
3.1
22
0.2
0.3
0.2
362
351
250
5.8
5.8
7.4
4.4
47
62
34
338
290
230
28
34
21
60
162
190
136
640
643
485
10.8
12
16
9.1
6.9
10
4.5
1.5
1.5
1.0
0.1
0.1
0.1
16.7
8.1
8.1
4.1
13.8
8.3
Eastern-
67
Oceania
Northern-
Southern-
46
60
34
51
70
35
1.3
1.2
0.9
5.2
6.5
4.0
13
19
8.7
13
18
9.4
29
45
18
3.3
3.4
2.0
2.0
2.8
1.4
17
27
11
21
35
12
0.1
0.1
0.1
0.3
0.5
0.2
43
51
37
64
78
51
1.2
138
162
116
241
300
189
4.4
23
30
18
36
49
27
0.7
25
30
21
73
99
52
1.3
1.4
0.9
5.5
3.5
0.9
0.5
1.8
0.9
quantities). Consequently the use of residue estimates based on
crop yield should be restricted to generalized assessments of the
scale of the resource; not to specific estimates of high resolution in
space and time.
4.6. Uncertainty
Estimation of crop residue production and potentials is generally hampered by lack of information. Official statistics on agricultural residue production based on field measurements are only
available to a very limited degree; hence the need for models to
estimate the scale of the resource and consequently uncertainty of
the estimates. The methodological approach taken in this analysis
entails uncertainty in the parameterization of the relation between
crop yield and residue production (3). Figs. 1, 2 and 4 and Tables 3e
5 provides 95% confidence limits to the estimates of residue production and derived values of component production and energy
potential. Particularly for maize and rice the estimated global
production is associated with significant uncertainty, with upper
confidence limit þ46e48% and lower confidence limit -34-35% off
the central estimate. Corresponding values for barley and soybean
are þ35e36% and 28%, and for wheat þ19% and 17%. Uncertainty is also associated with residue production from sugar cane.
However, with the approach taken in this analysis the uncertainty
and thus confidence intervals cannot be estimated. We haven’t
found published empirical data on sugar cane residue production to
support estimates of the uncertainty in the residue to product ratio.
Reduced uncertainty is desirable if estimates of agricultural
residue production are to be used in industrial production planning. If agricultural residues are expected to play a larger role in
future energy and material provision, it urgently calls for generally
available statistics on residue production based on measurements
in the field.
4.7. Current use of agricultural residues
Agricultural residues may have a multitude of functions in society [53]; either for nutrient recycling and soil amelioration (on-
Western-
Australia and
new Zealand
Melanesia
Micronesia
Polynesia
56
71
43
95
134
67
1.7
7.7
9.6
5.9
0.7
1.0
0.4
0.4
0.5
0.2
0.1
0.1
0.1
5.4
25
28
21
39
45
33
0.7
0.5
0.5
0.5
9.9 E-03
3.6 E-04
3.5 E04
2.7 E04
6.0 E-06
4.6 E-04
4.6 E04
4.6 E04
8.8 E-06
2.5
1.2
0.8
0.6
10.2E-03
9.6 E03
7.6 E06
4.6 E06
8.8 E06
8.8 E06
0.5
farm management), for bedding and feed, energy services or materials (on and off-farm). Very little information exists on how
residues are actually used. Statistics from Denmark [90] show that
allocation of the resource to different purposes varies even within
the group of cereals (Table 5), which makes it questionable to apply
general assumptions on residue use to a wider spectrum of agricultural residues and locations.
A number of modeling studies have estimated the use of crop
residues. Krausmann et al. [62] find that out of a total production of
4.4 Tg yr1, 2.9 Tg (66%) is appropriated for various purposes (fodder,
bedding and energy). They find considerable regional difference in
the fraction of residue production harvested and used, from 29% in
Sub-Saharan Africa to 90% in Western Europe. Also they find regional
variation in the fraction of harvested residues allocated to feeding
livestock from 10% or below in Europe and North America and Oceania to 83% in South and Central Asia. The fraction allocated to feed
appear negatively correlated to the fraction harvested as might be
expected. The findings by Krausmann et al. are corroborated by
Rogner et al. [70] estimating the amount of agricultural residues
harvested in 2000 to 54.3 EJ, corresponding to 2.9 Tg. A study by
Wirsenius et al. [64] find on a global level that 41% of the total residue
production is appropriated in the food system, i.e. as livestock feed.
29% is allocated to other purposes, e.g. energy, and 22% not harvested.
The remainder is lost in distribution and storage. Regional variation
in harvested fraction of the total residue production may be attributable to regional variation in crops grown. Lal [61] suggests that
residues from cereals and sugar cane are the most suitable for harvest. If so, regions with a relatively high proportion of their agricultural area covered with cereals or sugar cane e.g. Europe and North
America (regarding cereals) could potentially harvest a larger fraction of the total residue production. This assertion is supported by
results from Krausmann et al. [62]. Significantly different assumptions are used by Fujino et al. [18] and Yamamoto et al. [21] in their
assessment of global biomass resources. They assume that 0% of
harvestable agricultural residues are used currently and therefore
100% of the resource is available for energy purposes.
The findings by Krausmann et al. [62] that up to 90% of the
crop residues are used in Western Europe are not supported by
68
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Fig. 5. Estimated current (2006e08) production of cellulose (panel a), hemicellulose (panel b) and lignin (panel c) from residues of barley, maize, rice, soybean, sugar cane and
wheat.
other modeling studies and census reports. Weiser et al. [91] find
for Germany that 24% of the harvestable cereal straw production
is used for livestock husbandry and insignificant amounts used
for energy purposes. On the European Union level, Scarlat et al.
[23] find that between 1/5 to 1/3 of harvestable crop residues are
used for livestock and little for energy. Similar assumptions (1/3
of harvestable residues used for livestock) are made by Ericsson
et al. [16]. Denmark is one of few exceptions considering the use
of agricultural residues in advanced energy supply [16,23], with
20e40% of the crop residues from cereal production used for
energy (see Table 5). Still, only up to 60% of the total residue
production is harvested and used for livestock or energy
purposes [92]. For the US, the ‘Billion ton annual supply study’
[93] and its update [94] report an annual crop residue production
of 550 million metric tons dry matter; a more recent study report
the annual production to 518 Tg (¼ million metric tons) dry
matter [95]. 5.6 million metric tons corn stover is used for energy
corresponding to w1% of the total production. The amount used
for other purposes is not reported but the ‘billion ton annual
supply study’ indicates use rates well below 20% of the total
agricultural residue production.
Across modeling studies and national statistics a total global
human appropriation of crop residues of 40e70% appears, however,
with considerable variation between crops, regions and purposes.
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
69
Fig. 6. Geographical distribution of potential additional residue production attainable through agricultural intensification.
4.8. Resource availability
The results presented here provide an estimate of the amount of
crop residues theoretically available from agricultural land that is
harvested. Thus we present an upper limit of the amount of crop
residues to support energy, material and agronomic services. The
amount of residue available ecologically or economically is a fraction hereof.
Crop residues offer a number of benefits to soil quality, carbon
sequestration, erosion control and crop yields. Particularly on
degraded soils residue retention is beneficial, but removal has an
impact on soil quality in all regions and climates, although much
higher in tropical than temperate climates [96,97]. Removal of crop
residues in tropical and temperate semi-arid climates tends to
decrease carbon content in soil. Similar practice in cold and humid
climates does not have the same effect on soil carbon [98]. A positive correlation between carbon content in soil and soil productivity is demonstrated for various crops in different regions [97,99e
103]. On the other hand it is shown that differences in crop yield
induced by differences in soil organic carbon (SOC) content in many
cases can be overcome by appropriate supply of mineral fertilizers
[104,105] unless differences in soil organic carbon content are
substantial [104]. Recycling of by-products from biomass conversion is an option to mitigate the negative effects of biomass
extraction. Ash recycling from biomass combustion can return a
fraction of primarily the phosphorous and potassium extracted
from the soil. The char fraction from gasified or pyrolyzed biomass
furthermore contain a fraction of the carbon extracted with the
biomass in a more stable form.
The complexity of residue removal, soil organic carbon and crop
yield interactions restrict specific estimates of sustainable (from an
SOC point of view) recovery rates to compartment, field or subfield
levels [100,106], disabling meaningful estimates at the geographical
scale used here. In general, sustainable recovery rates correlates
positively with soil organic carbon content and negatively with
temperature and aridity. Regions characterized by low SOC soils
(<18 t ha1 SOC) are Western, Northern and South Africa; Western
and Central Asia; Australia; southern part of South America and
parts of mid-west US [107]. Arid climates according to the Köeppen-Geiger classification are found in some of the same regions;
Northern, Western and parts of Southern Africa; Western and parts
of Central Asia; Central US, Mexico and South-Central South
America [108]. Literature presents sustainable recovery rates of
agricultural residues between nothing [109] and everything [110],
Fig. 7. Estimates of the global agricultural residue production from cereals and sugar cane relative to the reference year applied in different studies. Reference data from Lal [61],
Smil [53], Krausmann et al. [62], and Hakala et al. [63]. For comparability estimates by Krausmann et al. and Hakala have been multiplied by 0.80 under the assumption that this
study cover 80% of the total global residue production, which they report.
70
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Table 4
Potential additional annual production of crop residue dry matter, which is realizable through agricultural intensification. Quantities in Tg (million tons) dry matter per year.
Missing numbers indicate values below 0.05.
Africa
America
EasternBarley
Upper confidence limit
Lower confidence limit
Maize
Upper confidence limit
Lower confidence limit
Rice
Upper confidence limit
Lower confidence limit
Soybean
Upper confidence limit
Lower confidence limit
Sugar cane
Wheat
Upper confidence limit
Lower confidence limit
Total
Upper confidence limit
Lower confidence limit
Middle -
2.4
4.0
1.3
87
160
45
9.4
16
5.2
1.1
1.6
0.7
2.0
5.0
6.7
3.7
107
190
58
14
26
7.8
0.9
1.5
0.5
0.1
0.1
0.0
1.6
17
29
9.9
Northern-
Southern-
10
16
6.2
0.4
0.7
0.2
0.1
0.2
0.0
10
19
4.6
18
24
14
29
41
20
0.4
0.6
0.2
0.8
1.3
1.7
0.9
12
22
6.5
with a trend towards recovery rates of general validity for the crops
included here between 25 and 60% [16,19,21e23,111,112]. Specific
thresholds require that models are refined to include the site
specificity of crop and soil interactions [100].
In this study, we show that there are considerable biomass resources that are technically and, most likely, also ecologically
available among agricultural residues. It is an open question,
however, how much of these resources can be considered
economically available or relevant. There is a large and growing
international trade in wood residues for fuel and refineries, which
dwarfs the corresponding international trade in other agricultural
biomass residues for fuel and refineries [113e115]. Agricultural
biomass residues for energy purposes appear to be mainly traded
locally or regionally in the nations states. Indeed, in e.g. Denmark
and other European countries, large amounts of straw deliveries are
contracted each year for the local combined heat and power plants.
Part of the explanation for this pattern, and the difference to the
market for wood residues, is undoubtedly found in the differences
in production system and hence cost of making these resources
economically available and relevant for the energy sector internationally e.g. seasonal variability for agricultural residues and the
ability of wood to be stored at the stump for years at low or even
negative costs. For the biomass resources in agricultural residues to
be truly economically relevant for the energy sector, locally,
regionally and internationally, these challenges must be solved by
the market agents to a degree that the resource can compete with
alternatives. Increased demand for biomass of all forms for the
bioenergy and refinery sectors will of course in itself make more of
these resources economically relevant as relative prices increase. In
turn, this should increase the incentive for investing in the
Table 5
Relative allocation of barley and wheat straw to different purposes in Denmark.
Average values for 2006e2008 based on [90].
Energy
Feed
Bedding
Not harvested
Barley
Wheat
20%
40%
16%
24%
38%
12%
11%
38%
Western-
48
92
23
27
45
16
2.4
3.6
1.5
0.6
0.1
0.2
0.1
79
142
41
Caribbean
2.8
4.6
1.6
0.9
1.7
0.4
2.9
6.6
9.2
4.9
Central0.4
0.7
0.2
38
76
16
0.9
1.8
0.4
0.1
0.2
0.1
0.8
0.1
0.1
0.1
40
80
18
North-
South-
6.0
14
1.9
1.4
2.6
0.7
61
130
23
8.9
17
3.9
11
20
5.9
1.0
19
26
13
103
197
48
2.0
3.4
1.1
73
109
46
81
127
49
development of new technologies to remedy and reduce the above
challenges and cost elements.
4.9. Other biomass resources
Global terrestrial net primary production (NPP) aboveground is
estimated to 33.54 Pg yr1 of carbon [116]. With an average carbon
content of biomass of 45e50% [117] this corresponds to 67e74 Pg
(billion tons) of dry biomass. Annual human appropriation of
aboveground terrestrial biomass (HANPP) is estimated to 10.2 Pg
carbon corresponding to 29% of NPP [116].
In light of global NPP and HANPP production of agricultural
residues is a marginal fraction. It may be argued that the human
appropriation of crop residues equals production as; no matter how
residues are used they have a function in society, be it feed/fodder,
building material, fuel, soil amelioration, erosion prevention etc.
Forests are estimated to account for 48% of terrestrial NPP
compared to 14% from cultivation [118] and forests are seen as one
of the major potential suppliers of biomass for energy and material
services [12,21,119]. Many studies point to energy crops on abandoned agricultural land as being the biggest resource of future
biomass supply for energy [15,16,21,68,120e123], with major
regional variation.
5. Conclusions
In this paper we developed a model to estimate the production
of crop residues from six globally important crops. The model is
based on an exponential relation between crop yield and residue
production, and represents a methodological development that to a
higher degree is based on crop physiology and empirical evidence
from a range of agricultural experiments.
In applying the model on crop yields averaged from 2006 to 08
we find the global production of crop residues from barley, maize,
1
rice, soybean, sugar cane and wheat to 3:7þ1:3
1:0 Pg dry matter yr .
Ecological and economic availability of agricultural residues is not
determined specifically but substantial amounts of biomass are
probably available on short term from land areas already under
some level of agricultural management. This may reduce the
N.S. Bentsen et al. / Progress in Energy and Combustion Science 40 (2014) 59e73
Asia
Europe
71
Oceania
Central- Eastern- South-Eastern- Southern- Western- Eastern- Northern- Southern- Western- Australia and new Zealand Melanesia Micronesia Polynesia
3.8
5.8
2.4
29
36
23
33
42
26
1.0
2.6
0.3
48
114
14
15
36
4.6
21
33
12
0.2
22
36
12
107
222
43
17
31
8.6
102
195
46
2.1
3.1
1.3
0.9
0.2
0.3
0.2
122
230
57
4.7
7.9
2.5
52
104
24
145
275
67
29
44
18
1.9
37
48
28
269
481
142
14
26
7.2
0.8
1.7
0.3
31
69
13
14
24
6.8
2.4
8.0
0.7
4.8
7.5
2.9
4.6
11
1.5
1.7
3.8
0.6
0.7
6.1
0.0
11
20
6.0
0.1
0.3
0.0
0.1
0.1
0.0
0.3
34
48
24
49
76
31
98
143
65
148
245
88
2.1
4.2
1.0
4.5
12
1.7
impacts from land use change if or when the biomass is harvested
and used for energy or material purposes displacing fossil
resources.
There is considerable geographic variation in the production of
agricultural residues, and also in ecological constraints for recovering these residues. Regions where particular attention on sustainability is required are Western, Northern and South Africa;
Western and Central Asia and South-Central South America.
Most regions of the world have a potential for increasing the
production of agricultural residues through development towards
high input agricultural management. As a global total we estimate
1
the additional potential to 1:3þ1:0
0:6 Pg yr . Northern, Western and
Southern Europe already apply high input agriculture and have
little potential for increasing residue production. Over time,
changing climates and diets may influence the potential for
increased residue production and may counteract as well as support increased production.
Lack of available data on agricultural residue production is a
significant barrier for the development of accurate models on residue production. If agricultural residues are expected to play a
larger role in future energy and material provision, reliable statistics are needed.
Acknowledgments
This paper is made as part of the CEESA project (www.ceesa.dk)
funded by the Danish Council for Strategic Research. The authors
thank Patrik Karlsson Nyed, University of Copenhagen, Denmark for
help with GIS based figures. Also the authors appreciate valuable
input and suggestions from the three anonymous reviewers.
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